| | |
|---|
| | | default=0, type=int) |
|---|
| | | parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.', |
|---|
| | | default=5, type=int) |
|---|
| | | parser.add_argument('--num_epochs_ft', dest='num_epochs_ft', help='Maximum number of finetuning epochs.', |
|---|
| | | default=5, type=int) |
|---|
| | | parser.add_argument('--batch_size', dest='batch_size', help='Batch size.', |
|---|
| | | default=16, type=int) |
|---|
| | | parser.add_argument('--lr', dest='lr', help='Base learning rate.', |
|---|
| | |
|---|
| | | default='', type=str) |
|---|
| | | parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.', |
|---|
| | | default='', type=str) |
|---|
| | | |
|---|
| | | parser.add_argument('--output_string', dest='output_string', help='String appended to output snapshots.', default = '', type=str) |
|---|
| | | parser.add_argument('--alpha', dest='alpha', help='Regression loss coefficient.', |
|---|
| | | default=0.001, type=float) |
|---|
| | | parser.add_argument('--iter_ref', dest='iter_ref', help='Number of iterative refinement passes.', |
|---|
| | | default=1, type=int) |
|---|
| | | parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='Pose_300W_LP', type=str) |
|---|
| | | args = parser.parse_args() |
|---|
| | | |
|---|
| | | return args |
|---|
| | | |
|---|
| | | def get_ignored_params(model): |
|---|
| | |
|---|
| | | b = [] |
|---|
| | | b.append(model.conv1) |
|---|
| | | b.append(model.bn1) |
|---|
| | | for i in range(len(b)): |
|---|
| | | for module_name, module in b[i].named_modules(): |
|---|
| | | if 'bn' in module_name: |
|---|
| | | module.eval() |
|---|
| | | for name, param in module.named_parameters(): |
|---|
| | | yield param |
|---|
| | | |
|---|
| | | def get_non_ignored_params(model): |
|---|
| | | # Generator function that yields params that will be optimized. |
|---|
| | | b = [] |
|---|
| | | b.append(model.layer1) |
|---|
| | | b.append(model.layer2) |
|---|
| | | b.append(model.layer3) |
|---|
| | | b.append(model.layer4) |
|---|
| | | for i in range(len(b)): |
|---|
| | | for j in b[i].modules(): |
|---|
| | | for k in j.parameters(): |
|---|
| | | yield k |
|---|
| | | for module_name, module in b[i].named_modules(): |
|---|
| | | if 'bn' in module_name: |
|---|
| | | module.eval() |
|---|
| | | for name, param in module.named_parameters(): |
|---|
| | | yield param |
|---|
| | | |
|---|
| | | def get_non_ignored_params(model): |
|---|
| | | # Generator function that yields params that will be optimized. |
|---|
| | | def get_fc_params(model): |
|---|
| | | b = [] |
|---|
| | | b.append(model.fc_yaw) |
|---|
| | | b.append(model.fc_pitch) |
|---|
| | | b.append(model.fc_roll) |
|---|
| | | b.append(model.fc_finetune) |
|---|
| | | for i in range(len(b)): |
|---|
| | | for j in b[i].modules(): |
|---|
| | | for k in j.parameters(): |
|---|
| | | yield k |
|---|
| | | for module_name, module in b[i].named_modules(): |
|---|
| | | for name, param in module.named_parameters(): |
|---|
| | | yield param |
|---|
| | | |
|---|
| | | def load_filtered_state_dict(model, snapshot): |
|---|
| | | # By user apaszke from discuss.pytorch.org |
|---|
| | |
|---|
| | | |
|---|
| | | cudnn.enabled = True |
|---|
| | | num_epochs = args.num_epochs |
|---|
| | | num_epochs_ft = args.num_epochs_ft |
|---|
| | | batch_size = args.batch_size |
|---|
| | | gpu = args.gpu_id |
|---|
| | | |
|---|
| | |
|---|
| | | # ResNet101 with 3 outputs |
|---|
| | | # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66) |
|---|
| | | # ResNet50 |
|---|
| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66) |
|---|
| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66, args.iter_ref) |
|---|
| | | # ResNet18 |
|---|
| | | # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66) |
|---|
| | | load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50'])) |
|---|
| | | |
|---|
| | | print 'Loading data.' |
|---|
| | | |
|---|
| | | transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224), |
|---|
| | | transforms.ToTensor()]) |
|---|
| | | transformations = transforms.Compose([transforms.Scale(240), |
|---|
| | | transforms.RandomCrop(224), transforms.ToTensor(), |
|---|
| | | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) |
|---|
| | | |
|---|
| | | pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, |
|---|
| | | transformations) |
|---|
| | | if args.dataset == 'Pose_300W_LP': |
|---|
| | | pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations) |
|---|
| | | elif args.dataset == 'AFLW2000': |
|---|
| | | pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations) |
|---|
| | | elif args.dataset == 'BIWI': |
|---|
| | | pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations) |
|---|
| | | elif args.dataset == 'AFLW': |
|---|
| | | pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations) |
|---|
| | | elif args.dataset == 'AFLW_aug': |
|---|
| | | pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations) |
|---|
| | | elif args.dataset == 'AFW': |
|---|
| | | pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations) |
|---|
| | | else: |
|---|
| | | print 'Error: not a valid dataset name' |
|---|
| | | sys.exit() |
|---|
| | | train_loader = torch.utils.data.DataLoader(dataset=pose_dataset, |
|---|
| | | batch_size=batch_size, |
|---|
| | | shuffle=True, |
|---|
| | | num_workers=2) |
|---|
| | | |
|---|
| | | model.cuda(gpu) |
|---|
| | | softmax = nn.Softmax() |
|---|
| | | criterion = nn.CrossEntropyLoss().cuda() |
|---|
| | | reg_criterion = nn.MSELoss().cuda() |
|---|
| | | smooth_l1_loss = nn.SmoothL1Loss().cuda() |
|---|
| | | # Regression loss coefficient |
|---|
| | | alpha = 0.01 |
|---|
| | | alpha = args.alpha |
|---|
| | | |
|---|
| | | idx_tensor = [idx for idx in xrange(66)] |
|---|
| | | idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu) |
|---|
| | | idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu) |
|---|
| | | |
|---|
| | | optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': args.lr}, |
|---|
| | | {'params': get_non_ignored_params(model), 'lr': args.lr * 10}], |
|---|
| | | lr = args.lr) |
|---|
| | | optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0}, |
|---|
| | | {'params': get_non_ignored_params(model), 'lr': args.lr}, |
|---|
| | | {'params': get_fc_params(model), 'lr': args.lr * 5}], |
|---|
| | | lr = args.lr) |
|---|
| | | |
|---|
| | | print 'Ready to train network.' |
|---|
| | | |
|---|
| | | print 'First phase of training.' |
|---|
| | | for epoch in range(num_epochs): |
|---|
| | | for i, (images, labels, name) in enumerate(train_loader): |
|---|
| | | for i, (images, labels, cont_labels, name) in enumerate(train_loader): |
|---|
| | | images = Variable(images.cuda(gpu)) |
|---|
| | | label_yaw = Variable(labels[:,0].cuda(gpu)) |
|---|
| | | label_pitch = Variable(labels[:,1].cuda(gpu)) |
|---|
| | | label_roll = Variable(labels[:,2].cuda(gpu)) |
|---|
| | | |
|---|
| | | label_angles = Variable(cont_labels[:,:3].cuda(gpu)) |
|---|
| | | label_yaw_cont = Variable(cont_labels[:,0].cuda(gpu)) |
|---|
| | | label_pitch_cont = Variable(cont_labels[:,1].cuda(gpu)) |
|---|
| | | label_roll_cont = Variable(cont_labels[:,2].cuda(gpu)) |
|---|
| | | |
|---|
| | | optimizer.zero_grad() |
|---|
| | | model.zero_grad() |
|---|
| | | |
|---|
| | | yaw, pitch, roll = model(images) |
|---|
| | | pre_yaw, pre_pitch, pre_roll, angles = model(images) |
|---|
| | | |
|---|
| | | # Cross entropy loss |
|---|
| | | loss_yaw = criterion(yaw, label_yaw) |
|---|
| | | loss_pitch = criterion(pitch, label_pitch) |
|---|
| | | loss_roll = criterion(roll, label_roll) |
|---|
| | | loss_yaw = criterion(pre_yaw, label_yaw) |
|---|
| | | loss_pitch = criterion(pre_pitch, label_pitch) |
|---|
| | | loss_roll = criterion(pre_roll, label_roll) |
|---|
| | | |
|---|
| | | # MSE loss |
|---|
| | | yaw_predicted = F.softmax(yaw) |
|---|
| | | pitch_predicted = F.softmax(pitch) |
|---|
| | | roll_predicted = F.softmax(roll) |
|---|
| | | yaw_predicted = softmax(pre_yaw) |
|---|
| | | pitch_predicted = softmax(pre_pitch) |
|---|
| | | roll_predicted = softmax(pre_roll) |
|---|
| | | |
|---|
| | | yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1) |
|---|
| | | pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1) |
|---|
| | | roll_predicted = torch.sum(roll_predicted.data * idx_tensor, 1) |
|---|
| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99 |
|---|
| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99 |
|---|
| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99 |
|---|
| | | |
|---|
| | | loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float()) |
|---|
| | | loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float()) |
|---|
| | | loss_reg_roll = reg_criterion(roll_predicted, label_roll.float()) |
|---|
| | | loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont) |
|---|
| | | loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont) |
|---|
| | | loss_reg_roll = reg_criterion(roll_predicted, label_roll_cont) |
|---|
| | | |
|---|
| | | # Total loss |
|---|
| | | loss_yaw += alpha * loss_reg_yaw |
|---|
| | |
|---|
| | | torch.autograd.backward(loss_seq, grad_seq) |
|---|
| | | optimizer.step() |
|---|
| | | |
|---|
| | | # print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f' |
|---|
| | | # %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0])) |
|---|
| | | |
|---|
| | | if (i+1) % 100 == 0: |
|---|
| | | print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f' |
|---|
| | | %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0])) |
|---|
| | | if epoch == 0: |
|---|
| | | torch.save(model.state_dict(), |
|---|
| | | 'output/snapshots/resnet50_iter_'+ str(i+1) + '.pkl') |
|---|
| | | # if epoch == 0: |
|---|
| | | # torch.save(model.state_dict(), |
|---|
| | | # 'output/snapshots/' + args.output_string + '_iter_'+ str(i+1) + '.pkl') |
|---|
| | | |
|---|
| | | # Save models at numbered epochs. |
|---|
| | | if epoch % 1 == 0 and epoch < num_epochs - 1: |
|---|
| | | if epoch % 1 == 0 and epoch < num_epochs: |
|---|
| | | print 'Taking snapshot...' |
|---|
| | | torch.save(model.state_dict(), |
|---|
| | | 'output/snapshots/resnet50_epoch_'+ str(epoch+1) + '.pkl') |
|---|
| | | 'output/snapshots/' + args.output_string + '_epoch_'+ str(epoch+1) + '.pkl') |
|---|
| | | |
|---|
| | | # Save the final Trained Model |
|---|
| | | torch.save(model.state_dict(), 'output/snapshots/resnet50_epoch' + str(epoch+1) + '.pkl') |
|---|
| | | print 'Second phase of training (finetuning layer).' |
|---|
| | | for epoch in range(num_epochs_ft): |
|---|
| | | for i, (images, labels, cont_labels, name) in enumerate(train_loader): |
|---|
| | | images = Variable(images.cuda(gpu)) |
|---|
| | | label_yaw = Variable(labels[:,0].cuda(gpu)) |
|---|
| | | label_pitch = Variable(labels[:,1].cuda(gpu)) |
|---|
| | | label_roll = Variable(labels[:,2].cuda(gpu)) |
|---|
| | | |
|---|
| | | label_angles = Variable(cont_labels[:,:3].cuda(gpu)) |
|---|
| | | label_yaw_cont = Variable(cont_labels[:,0].cuda(gpu)) |
|---|
| | | label_pitch_cont = Variable(cont_labels[:,1].cuda(gpu)) |
|---|
| | | label_roll_cont = Variable(cont_labels[:,2].cuda(gpu)) |
|---|
| | | |
|---|
| | | optimizer.zero_grad() |
|---|
| | | model.zero_grad() |
|---|
| | | |
|---|
| | | pre_yaw, pre_pitch, pre_roll, angles = model(images) |
|---|
| | | |
|---|
| | | # Cross entropy loss |
|---|
| | | loss_yaw = criterion(pre_yaw, label_yaw) |
|---|
| | | loss_pitch = criterion(pre_pitch, label_pitch) |
|---|
| | | loss_roll = criterion(pre_roll, label_roll) |
|---|
| | | |
|---|
| | | # MSE loss |
|---|
| | | yaw_predicted = softmax(pre_yaw) |
|---|
| | | pitch_predicted = softmax(pre_pitch) |
|---|
| | | roll_predicted = softmax(pre_roll) |
|---|
| | | |
|---|
| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99 |
|---|
| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99 |
|---|
| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99 |
|---|
| | | |
|---|
| | | loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont) |
|---|
| | | loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont) |
|---|
| | | loss_reg_roll = reg_criterion(roll_predicted, label_roll_cont) |
|---|
| | | |
|---|
| | | # Total loss |
|---|
| | | loss_yaw += alpha * loss_reg_yaw |
|---|
| | | loss_pitch += alpha * loss_reg_pitch |
|---|
| | | loss_roll += alpha * loss_reg_roll |
|---|
| | | |
|---|
| | | # Finetuning loss |
|---|
| | | loss_seq = [loss_yaw, loss_pitch, loss_roll] |
|---|
| | | for idx in xrange(1,len(angles)): |
|---|
| | | label_angles_residuals = label_angles - angles[0] * 3 - 99 |
|---|
| | | for idy in xrange(1,idx): |
|---|
| | | label_angles_residuals += angles[idy] * 3 - 99 |
|---|
| | | label_angles_residuals = label_angles_residuals.detach() |
|---|
| | | # Reconvert to other unit |
|---|
| | | label_angles_residuals = label_angles_residuals / 3.0 + 33 |
|---|
| | | loss_angles = smooth_l1_loss(angles[idx], label_angles_residuals) |
|---|
| | | loss_seq.append(loss_angles) |
|---|
| | | |
|---|
| | | grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))] |
|---|
| | | torch.autograd.backward(loss_seq, grad_seq) |
|---|
| | | optimizer.step() |
|---|
| | | |
|---|
| | | if (i+1) % 100 == 0: |
|---|
| | | print ('Epoch [%d/%d], Iter [%d/%d] Losses: pre-yaw %.4f, pre-pitch %.4f, pre-roll %.4f, finetuning %.4f' |
|---|
| | | %(epoch+1, num_epochs_ft, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0], loss_angles.data[0])) |
|---|
| | | # if epoch == 0: |
|---|
| | | # torch.save(model.state_dict(), |
|---|
| | | # 'output/snapshots/' + args.output_string + '_iter_'+ str(i+1) + '.pkl') |
|---|
| | | |
|---|
| | | # Save models at numbered epochs. |
|---|
| | | if epoch % 1 == 0 and epoch < num_epochs_ft: |
|---|
| | | print 'Taking snapshot...' |
|---|
| | | torch.save(model.state_dict(), |
|---|
| | | 'output/snapshots/' + args.output_string + '_epoch_'+ str(num_epochs+epoch+1) + '.pkl') |
|---|
